
AI-generated reporting significantly reduces radiologists' reading times and increases report acceptability over time.
Key Details
- 1Study published in JACR evaluated AI-generated reports for chest X-rays.
- 2Researchers used Kakao Brain’s KARA-CXR model on a dataset of 756 radiographs.
- 3Five radiologists generated preliminary AI reports; two assessed acceptability and need for revisions.
- 4Human approval rates of AI-generated reports increased, indicating growing trust.
- 5Experts call for further study into improving preliminary AI reports and their impact on diagnostics.
Why It Matters
Streamlining reporting with AI could improve radiologist efficiency and address workforce pressures. Increased acceptability suggests AI integration is advancing, but accuracy and clinical impact remain focus areas for future research.

Source
Radiology Business
Related News

•AuntMinnie
Head-to-Head Study Evaluates AI Accuracy in Fracture Detection on X-Ray
A prospective study compared three commercial AI tools for fracture detection on x-ray, showing moderate-to-high accuracy for simple cases but weaker performance in complex scenarios.

•AuntMinnie
AI Boosts Agreement in CAD-RADS Classification on Cardiac CT
Deep learning AI improves interreader agreement in CAD-RADS assessments on coronary CT angiography.

•Radiology Business
AI Automates Head CT Reformatting, Improving Efficiency and Consistency
Researchers at UC Irvine used deep learning to automate head CT reformatting, improving workflow standardization and efficiency.